2018-09-01

whoami

whoami

whoami

whoami

Data scientist @ funda

MSc in Applied Statistics

thatssorandom.com

@edwin_thoen

CRAN: padr, GGally, recipes

What is this talk about?

Lets show some hands

Who:

Lets show some hands

Who:

  • is very experienced with using NSE (base, tidyeval, or both)?

Lets show some hands

Who:

  • is very experienced with using NSE (base, tidyeval, or both)?
  • applies it now and then, but not with great comfort?

Lets show some hands

Who:

  • is very experienced with using NSE (base, tidyeval, or both)?
  • applies it now and then, but not with great comfort?
  • only knows it at face value?

Lets show some hands

Who:

  • is very experienced with using NSE (base, tidyeval, or both)?
  • applies it now and then, but not with great comfort?
  • only knows it at face value?
  • has hardly a clue what it is?

Why do we love R so much for data-analysis?

Why do we love R so much for data-analysis?

We don't have to use R when using R!

Why do we love R so much for data-analysis?

We don't have to use R when using R!

We can do

library(dplyr)
mtcars <- mtcars %>% mutate(cyl_drat = cyl + drat)

or

mtcars_dt <- data.table::as.data.table(mtcars)
mtcars_dt[, cyl_drat := cyl + drat]

Instead of

mtcars$cyl_drat <- mtcars$cyl + mtcars$drat

We all use NSE!

When you started using R, did you mix up?

install.packages("padr")

and

library(padr)

We all use NSE!

When you started using R, did you mix up?

install.packages("padr")

and

library(padr)

Or wondered why the library(padr) worked. Even when there is no variable callend padr?

We all use NSE!

Apparantly, things that ought not to work, are working.

This results in a language full of magic:

subset(mtcars, cyl == 6)

ggplot2::ggplot(mtcars, aes(mpg, drat)) +
  geom_point()

data.table::as.data.table(mtcars)[ ,mean(mpg), by = cyl]

Why data analysts love it (and cs people don't)

R is designed to do data science. (Well, then it was still called statistics).

Flexibility to maiximize insight.

Enable DSL creation to tailor make tools to solve a specific problem without overhead.

With flexibility comes ambiguity and responsibility.

Why should you care about how NSE works?

Why should you care about how NSE works?

  • Because you are a geek who can't stand not knowing.

Why should you care about how NSE works?

  • Because you are a geek who can't stand not knowing.
  • Because most R users slip into tool design sooner or later.

What is standard in the first place?

my_val <- 123

my_func <- function(x) {
  x / 42 * 121
}

my_func(71)
## [1] 204.5476
my_func(my_val)
## [1] 354.3571
my_func(your_val)
## Error in my_func(your_val): object 'your_val' not found

What's in a NAME

By creating a variable we bind a value to a name.

my_val <- 123

123 is the value that is bound to the name my_val.

Binding happens in an environment, in this case the global.

What's in a NAME

my_val <- 123

123 is the value that is bound to the name my_val.

Binding happens in an environment, in this case the global.

Just call my name, I'll give you the value:

my_val
## [1] 123

This is evaluating the name.

Lexical scoping

R starts looking for the value of name in the environment the name is called in.

x <- "a variable in the global"
a_func <- function() {
  x <- "a variable in the local"
  x
}
a_func()
## [1] "a variable in the local"

Lexical scoping

When it can't find it locally, move up to the parent environment (where the current env was created).

z <- "a variable in the global"
another_func <- function() {
  z
}
another_func()
## [1] "a variable in the global"

Lexical scoping

Finally, an error is thrown when the variable can't be found.

nobody_loves_me <- function() {
  y
}
nobody_loves_me()
## Error in nobody_loves_me(): object 'y' not found

So this is standard evaluation in R.

Wait for it

We can also ask R to postpone judgement, by storing the variable name in a name object.

quote(my_unknown_var) %>% class()
## [1] "name"

Wait for it

We can also ask R to postpone judgement, by storing the variable name in a name object.

quote(my_unknown_var) %>% class()
## [1] "name"

This is the act of quoting, saving a variable name to be evaluated later.

(name is also called symbol)

Wait for it

Quoted variable names are not evaluated. It doesn't matter if they don't exist.

quoted_var <- quote(wait_for_it)
quoted_var
## wait_for_it
quoted_var %>% class()
## [1] "name"

Wait for it

Quoted variable names are not evaluated. It doesn't matter if they don't exist.

quoted_var <- quote(wait_for_it)
quoted_var
## wait_for_it
quoted_var %>% class()
## [1] "name"

We have two variables here:

The regular variable quoted_var contains the quoted variable wait_for_it.

Wait for it

It will start looking for the value only when we ask to evaluate it.

eval(quoted_var)
## Error in eval(quoted_var): object 'wait_for_it' not found

Wait for it

wait_for_it <- "I finally have a value"
eval(quoted_var)
## [1] "I finally have a value"

What is the use?

What is the use?

We can evaluate the name in a different environment.

Building our own pull

diy_pull <- function(x, name) {
  eval(name, envir = x)
}

diy_pull(mtcars, quote(cyl)) %>% head(5)
## [1] 6 6 4 6 8

Building our own pull

diy_pull <- function(x, name) {
  eval(name, envir = x)
}

diy_pull(mtcars, quote(cyl)) %>% head(5)
## [1] 6 6 4 6 8

Note that we can specify a data frame as environment. The column names can be called as variables within it.

Quoting inside the function

You'll never have to quote your function arguments when using a DSL.

mtcars %>% select(cyl)
as.data.table(mtcars)[, cyl]
ggplot(mtcars, aes(cyl)) + geom_bar()

Why does R not throw an error? There is no cyl in the global…

Lazy, lazy R

Lazy, lazy R

koala <- function(x, y) {
  x + 42
}

koala(3)
## [1] 45

Industrious Python

def koala(x, y):
  return(x + 42)
koala(3)
## TypeError: koala() takes exactly 2 arguments (1 given)
## 
## Detailed traceback: 
##   File "<string>", line 1, in <module>

Quoting inside a function

So, R doesn't make a fuss until it really has to.

This allows quoting inside functions.

diy_pull_2 <- function(x, bare_name) {
  name <- quote(bare_name)
  eval(name, env = x)
}

Quoting inside a function

So, R doesn't make a fuss until it really has to.

This allows quoting inside functions.

diy_pull_2 <- function(x, bare_name) {
  name <- quote(bare_name)
  eval(name, env = x)
}

diy_pull_2(mtcars, cyl)
## Error in eval(name, env = x): object 'cyl' not found

Quoting inside a function

quote does literally quote the input, but we want to quote the value at the argument, not the arg's name.

Here we need substitute:

substitute_example <- function(x) {
  substitute(x)
}
substitute_example(cyl)
## cyl
substitute_example(cyl) %>% class()
## [1] "name"

Quoting inside a function

diy_pull_2 <- function(x, bare_name) {
  name <- substitute(bare_name)
  eval(name, env = x)
}

diy_pull_2(mtcars, cyl)
##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

Not just names

We can quote the following things:

  • name: the name of an R object

  • call: calling of a function

  • pairlist: something from the past you shouldn't bother about

  • literal: evaluates to the value itself

Expressions: "don't be another SQL"

Call

Just like a name, a function call can be delayed by quoting.

my_little_filter <- function(x, 
                             call) {
  call_quoted <- substitute(call)
  retain_row  <- eval(call_quoted, envir = x)
  x[retain_row, ]
}

my_little_filter(mtcars, cyl == 4 & gear == 4) %>% head(2)
##    mpg cyl  disp hp drat   wt  qsec vs am gear carb cyl_drat
## 3 22.8   4 108.0 93 3.85 2.32 18.61  1  1    4    1     7.85
## 8 24.4   4 146.7 62 3.69 3.19 20.00  1  0    4    2     7.69

The promise you make

That's a promise

That's a promise

The value slot is empty at promise creation.

Only when the argument's expression is evaluated in the function, we start looking for it.

That's a promise

The value slot is empty at promise creation.

Only when the argument's expression is evaluated in the function, we start looking for it.

Remember koala?

koala <- function(x, y) {
  x + 42
}

Promising koala

When we call koala we create the following promise

x_value <- 42
koala(x = x_value)

Accessing the expression slot!

Now, that's why subsitute works!

Accesses the expression in the promise without evaluating it.

subs_func <- function(val) {
  vals_expr <- substitute(val)
  deparse(vals_expr)
}
subs_func(anything_goes)
## [1] "anything_goes"

Note that deparse coerces the expression to a character. Its inverse is parse.

Tidyeval

The tidyverse NSE dialect.

mtcars %>% select(cyl)

We now know that cyl gets somehow quoted by select and evaluated within the data frame.

But what if we want to wrap tidyverse code in a custom function?

Tidyeval - custom function

This won't work

my_tv_func <- function(x, grouping_var) {
  x %>% 
    group_by(grouping_var) %>% 
    summarise(max_drat = max(drat))
}
my_tv_func(mtcars, cyl)

Why?

Tidyeval - custom function

In order to get it to work:

  • quote the variable, like in regular R
  • unquote again before the argument is swallowed by the tidyverse function
  • then tidyverse function can go back and quote it again

Tidyeval - custom function

In order to get it to work:

  • quote the variable upfront
  • unquote again before the argument is swallowed by the tidyverse function
  • then tidyverse function can go back and quote it again
my_tv_func <- function(x, grouping_var) {
  x %>% 
    group_by(!!grouping_var) %>% 
    summarise(max_drat = max(drat))
}
my_tv_func(mtcars, quo(cyl))

Tidyeval - custom function

Just like using substitute you can quote the arguments value with enquo.

my_grouping_func <- function(x, grouping_var) {
  grouping_var_q <- enquo(grouping_var)
  x %>% 
    group_by(!!grouping_var_q) %>% 
    summarise(max_drat = max(drat))
}
my_grouping_func(mtcars, cyl)
## # A tibble: 3 x 2
##     cyl max_drat
##   <dbl>    <dbl>
## 1     4     4.93
## 2     6     3.92
## 3     8     4.22

Summary

Summary

my_correct_second_little_filter <- function(x, bare_call) {
  call <- substitute(bare_call)
  x[eval(call, envir = x), ]
}

my_correct_second_little_filter(mtcars, cyl == 4) %>% head(1)
##    mpg cyl disp hp drat   wt  qsec vs am gear carb cyl_drat
## 3 22.8   4  108 93 3.85 2.32 18.61  1  1    4    1     7.85
  • cyl == 4 on itself is invalid, there is no cyl in the global.
  • But, R refrains from judgement, stores it in a promise.
  • substitute gets the expression, which is the quoted call.
  • This expression is evaluated within the environment of x.
  • Here it is completely valid, because there is a cyl column.

Things not covered (extensively)

  • quasiquotation

  • quosures

  • environments

Thank You!

edwinthoen@gmail.com

@edwin_thoen

github.com/EdwinTh/satRday

edwinth.github.io/blog/nse

edwinth.github.io/blog/dplyr-recipes